Lung cancer kills more Americans each year than colorectal, breast, and prostate cancers combined. Over 173,000 new cases of lung cancer will be diagnosed and over 160,000 will die from this disease in 2004. The successful early detection and effective treatment of lung cancer focuses on its identification when it is a small lung nodule. As a result, when lung cancer is suspected, imaging with chest radiography or Computed Tomography (CT) scanning is the first step toward making the diagnosis. The tracking of lung nodules in scans acquired at different times for the same patient is useful in the determination of malignancy.
Because of its superior contrast resolution and cross-sectional acquisition, which effectively eliminates super-imposition of structures, CT is a more sensitive technique for detection of lung cancer than chest radiography. Unfortunately, CT is not specific for the diagnosis of lung cancer. In fact, most lung nodules do not represent malignancy, but are a mixture of benign lesions that range from normal lymph nodes to post-infectious granulomas to scars.
Lung registration is a non-rigid registration problem and has been studied for a long time. Most algorithms suffer from the prohibitive computation and/or errors introduced by unpredictable distortions at differing levels of inspiration. Although nodule shape and composition may correlate with malignancy/benignity, the correlation is not perfect and, instead, the most reliable means of differentiating benign from malignant nodules is serial CT follow-up and assessment for growth. Thus, when one or more nodules are found in a CT scan, which, today, may consist of several hundred cross-sectional images, they must be matched with their counterparts in one or more prior scans—a process that is time consuming and error prone.